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IEEE International Conference on Web services (ICWS)

作者： Tilei Gao Ming Yang Rong Jiang Tao Zhang Leijin Long School of Information Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Institute of Intelligence Applications Faculty of Information Engineering and Automation Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China School of Information Yunnan University of Finance and Economics Kunming China Yunnan Key Laboratory of Service Computing Kunming China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

In the context of the digital economy, cloud services have become the preferred digital solutions for enterprises. However, even cloud services guaranteed by international authoritative platforms may still have numerous trustworthiness issues. The trustworthiness of cloud services has become one of the unavoidable key issues in the digital transformation process of current enterprises. This article starts from the perspective of the risks faced by cloud services, analyzes the current situation of cloud service trustworthiness, and proposes a cloud service trustworthiness assessment method based on fuzzy analytic hierarchy process, risk matrix, and D-S theory. The method can effectively assess the trustworthiness status of cloud services. It has significant research significance and practical value for users choosing cloud services to build digital systems, and monitoring and managing the trustworthiness of currently used cloud services.

关键词： Cloud computing Accuracy Web services Digital transformation Computational modeling Analytic hierarchy process Risk management Information technology Standards Monitoring

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A hierarchical access control and sharing model for healthcare data with on-chain-off-chain collaboration

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Journal of Intelligent and Fuzzy Systems 2025年第3期48卷 215-231页

作者： Dong, Jianhang Jiang, Rong Zhang, Yi Tian, Shenghu Wu, Bin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Institute of Intelligence Applications Yunnan University of Finance and Economics Kunming China Yunnan Academy of Scientific and Technical Information Kunming China

Healthcare data is growing rapidly and data sharing is becoming increasingly important among hospitals. However, traditional methods are prone to data leakage and threaten patient privacy. To ensure data security and realize sharing and access control, combining blockchain technology, attribute-based encryption and ciphertext policy (CP-ABE) and AES symmetric encryption algorithm, this paper proposes a hierarchical attribute-based encryption algorithm based on master-sidechain (MSC-CP-ABE) and a collaborative access control and sharing model with on-chain and off-chain (AC-CSS). The algorithm encrypts hierarchically based on data sensitivity, the ciphertext is stored in the interplanetary file system (IPFS), and the encrypted address and key are stored in the side chain, which is convenient for indexing in the main chain. The vectorized access policy implements policy hiding, combines on-chain and off-chain, saves blockchain space, achieves hierarchical access control, and improves security and efficiency. Simulation experiments show that the main sidechain model proposed in this paper has better performance in terms of transaction delay and throughput. Compared with existing models, the algorithm proposed in this paper has certain advantages in data encryption and decryption time and key generation time. © 2025 – IOS Press. All rights reserved.

关键词： Authorization

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An Electronic Medical Record Access Control Model Based on Intuitionistic Fuzzy Trust

SSRN

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SSRN 2023年

作者： Jiang, Rong Liu, Rui Zhang, Tao Ding, Weiping Tian, Shenghu Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Institute of Intelligence Applications Yunnan University of Finance and Economics Kunming China School of Information Science and Technology Nantong University Nantong China

Electronic Medical Record (EMR), as the core data of medical big data, has improved the efficiency of medical services in the process of sharing and using, but a series of data privacy leakage problems have emerged. Access control technology can ensure users' legitimate and appropriate access to data resources, but traditional access control has many limitations such as static and coarse-grained, which is difficult to meet the dynamic and complex healthcare environment. Therefore, this article proposes an intuitionistic fuzzy trust-based access control model to solve the data privacy leakage problem in order to promote medical information sharing. The model proposes a new trust evaluation method that uses Latent Dirichlet Allocation (LDA) topic model and intuitionistic fuzzy theory to more accurately quantify doctors' trust;In trust aggregation, time windows and time decay are used to enhance the timeliness of trust aggregation;The model also adopts an intuitionistic fuzzy clustering algorithm based on relative entropy and a reward and punishment mechanism to achieve adaptive dynamic access control of doctors' access behavior. Experiments prove that the access control model based on intuitionistic fuzzy trust proposed in this article has a certain control effect on the privacy leakage problem of electronic medical record system. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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Second-Hand Car Trading Framework Based on Blockchain in Cloud service Environment

Second-Hand Car Trading Framework Based on Blockchain in Clo...

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Computers and Communications (ACCC), Asia Conference on

作者： Yimin Yu Chuanjia Yao Yi Zhang Rong Jiang Institute of Intelligence Applications Yunnan University of Finance and Economics Kunming China School of Business Yunnan University of Finance and Economics Kunming China Key Laboratory of Service Computing and Safety Management of Yunnan Provincial Universities Yunnan University of Finance and Economics Kunming China Kunming Key Laboratory of Information Economy & Information Management Yunnan University of Finance and Economics Kunming China

ISBN: (纸本)9781665407441

At present, the world economy is in recession, especially under the impact of the Covid-19 epidemic, China's economy has also been greatly impacted. In this context, the disposable personal income of residents has also declined to varying degrees. More and more people choose economical life. If they can buy a used car in good condition at a good price, they are less likely to buy a brand new one. Under such a consumption concept, China's demand for second-hand cars is increasing. However, although China's second-hand car industry has developed for more than 30 years and the market scale has gradually expanded, there are still many problems behind the prosperity of the second-hand car market. These problems have existed for a long time, leading to a lot of disputes, unhappiness, disappointment, and even threats to the lives of consumers. These long-term problems also affect the virtuous circle of the second-hand car market, and hinder the healthy development of China's economy to a certain extent. In the past, research work mainly focused on the role of new policies, relevant laws and vehicle management and traffic management functions, this paper introduces the blockchain technology, which has the advantages of non tampering, transparency and traceability. This paper attempts to use blockchain technology as an auxiliary means to solve the long-standing problems in the used car market. This paper proposes a framework of used car trading based on blockchain in cloud service environment, and explains the working principle of the framework. Finally, the future research work is prospected.

关键词： Asia Blockchains Automobiles Economics

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Unsupervised feature selection via adaptive embedding and sparse learning for Parkinson’s disease diagnosis 1

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3rd International Workshop on Connectomics in NeuroImaging, CNI 2019 held in conjunction with the 22nd International Conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2019

作者： Huang, Zhongwei Lei, Haijun Chen, Guoliang Li, Shiqi Li, Hancong Elazab, Ahmed Lei, Baiying Key Laboratory of Service Computing and Applications Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen518060 China

ISBN: (数字)9783030323912

ISBN: (纸本)9783030323905

Parkinson’s disease (PD) is known as a progressive neurodegenerative disease in elderly people. Apart from decelerating the disease exacerbation, early and accurate diagnosis also alleviates mental and physical sufferings and provides timely and appropriate medication. In this paper, we propose an unsupervised feature selection method via adaptive manifold embedding and sparse learning exploiting longitudinal multimodal neuroimaging data for classification and regression prediction. Specifically, the proposed method simultaneously carries out feature selection and dynamic local structure learning to obtain the structural information inherent in the neuroimaging data. We conduct extensive experiments on the publicly available Parkinson’s progression markers initiative (PPMI) dataset to validate the proposed method. Our proposed method outperforms other state-of-the-art methods in terms of classification and regression prediction performance. © Springer Nature Switzerland AG 2019.

关键词： Neuroimaging

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A Microfluidic Spherical Helix Module Using Liquid Metal and Additive Manufacturing for Drug Delivery applications

A Microfluidic Spherical Helix Module Using Liquid Metal and...

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Microwave, MTT-S International Symposium

作者： Yangyang Guan Shicong Wang Manos M. Tentzeris Yuanan Liu Beijing Key Laboratory of Work Safety Intelligent Monitoring Beijing University of Posts and Telecommunications China Key Laboratory of Embedded System and Service Computing of the Ministry of Education Tongji University China Agile Technologies for High-performance Electromagnetic Novel Applications Georgia Institute of Technology USA

In this paper, a spherical helix module based on 3D printing and microfluidic technologies was fabricated, tested and applied to drug delivery applications. A liquid metal alloy is introduced to enhance the sensitivity of the complex and drug-/liquid-reconfigurable helix topology. Moreover, the influence of the dimensions of the hemisphere inscribed by the helical turns on the resonant frequency of the structure is researched. The microfluidic channels with 1 mm radius are shaped in the form of double semispherical helix using a Form-2 3D printer. To fulfill effectively the liquid metal (LM) injection, movement and recycle for reuse, a syringe pump is integrated to flexibly control the flow of the fluid. Besides, the proof-of-concept prototype achieves excellent radiation gain and impedance matching. The realized sensitivity is 0.96 mL/GHz and 0.33 mL/GHz corresponding with the frequency shift from 2.28 to 2.01 GHz and 2.01 to 1.65 GHz.

关键词： Resonant frequency Monitoring Microfluidics Liquids Drugs Mathematical model Three-dimensional printing

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Semi-Supervised Learning in Large Scale Text Categorization

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Journal of Shanghai Jiaotong university(Science) 2017年第3期22卷 291-302页

作者：许泽文李建强刘博毕敬李蓉毛睿 School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for Io T Software and Systems Beijing University of Technology Guangdong Key Laboratory of Popular High Performance Computers Shenzhen University Shenzhen Key Laboratory of Service Computing and Applications Shenzhen University

The rapid development of the Internet brings a variety of original information including text information, audio information, etc. However, it is difficult to find the most useful knowledge rapidly and accurately because of its huge number. Automatic text classification technology based on machine learning can classify a large number of natural language documents into the corresponding subject categories according to its correct semantics. It is helpful to grasp the text information directly. By learning from a set of hand-labeled documents,we obtain the traditional supervised classifier for text categorization(TC). However, labeling all data by human is labor intensive and time consuming. To solve this problem, some scholars proposed a semi-supervised learning method to train classifier, but it is unfeasible for various kinds and great number of Web data since it still needs a part of hand-labeled data. In 2012, Li et al. invented a fully automatic categorization approach for text(FACT)based on supervised learning, where no manual labeling efforts are required. But automatically labeling all data can bring noise into experiment and cause the fact that the result cannot meet the accuracy requirement. We put forward a new idea that part of data with high accuracy can be automatically tagged based on the semantic of category name, then a semi-supervised way is taken to train classifier with both labeled and unlabeled data,and ultimately a precise classification of massive text data can be achieved. The empirical experiments show that the method outperforms the supervised support vector machine(SVM) in terms of both F1 performance and classification accuracy in most cases. It proves the effectiveness of the semi-supervised algorithm in automatic TC.

关键词： text data mining semi-supervised automatic tagging classifier

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JOINT DETECTION AND CLINICAL SCORE PREDICTION IN PARKINSON'S DISEASE VIA MULTI-MODAL SPARSE LEARNING

JOINT DETECTION AND CLINICAL SCORE PREDICTION IN PARKINSON'S...

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International Symposium on Biomedical Imaging

作者： Haijun Lei Jian Zhang Zhang Yang Ee-leng Tan Baiying Lei Qiuming Luo College of Computer Science and Software Engineering Shenzhen University Key Laboratory of Service Computing and Applications Guangdong Province Key Laboratory of Popular High Performance Computers School of Information Engineering Shenzhen University School of Electric and Electronic Engineering Nanyang Technological University School of Biomedical Engineering Shenzhen University Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound

ISBN: (纸本)9781509011735

In this study, a novel feature selection framework is proposed to simultaneously perform classification and clinical scores prediction of Parkinson's disease (PD) via multi-modal neuroimaging data. Specifically, a new feature selection model is devised to capture discriminative features to train support vector regression model for clinical scores (e.g., sleep scores and olfactory scores) prediction and support vector classification model for class label identification. Our method is evaluated on a public dataset of 208 subjects including 56 normal controls (NC), 123 PD and 29 scans without evidence of dopamine deficit (SWEDD) via a 10-fold cross-validation method. The experimental results demonstrate that multimodal data can effectively improve the performance in disease status identification and clinical scores prediction compared to one single modality. Our proposed method also outperforms the related methods.

关键词： Parkinson's disease Feature selection Classification Prediction Multi-modal data

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Global climate

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Bulletin of the American Meteorological Society 2020年第8期101卷 S9-S128页

作者： Dunn, R.J.H. Stanitski, D.M. Gobron, N. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom NOAA/OAR Earth System Research Laboratories Boulder Colorado European Commission Joint Research Centre Ispra Italy Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Met Office Hadley Centre Exeter United Kingdom University of Reading Reading United Kingdom Department of Atmospheric and Planetary Science Hampton University Hampton Virginia NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina University of Bremen Bremen Germany NOAA/OAR Earth System Research Laboratories Boulder Colorado Centro de Investigaciones sobre Desertificación - Spanish National Research Council Moncada (Valencia) Spain and Regional Climate Group Department of Earth Sciences University of Gothenburg Gothenburg Sweden Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile NOAA/OAR ESRL Global Monitoring Laboratory Boulder Colorado Department of Civil and Environmental Engineering Princeton University Princeton New Jersey Global Precipitation Climatology Centre Deutscher Wetterdienst Offenbach Germany University of Reading Reading United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom National Oceanography Centre Southampton United Kingdom School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université de Paris Institut de physique du globe de Paris CNRS IGN Paris France and ENSG-Géomatique IGN Marne-la-Vallée France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Sorbonne Université Paris France Universität Hamburg Hamburg Germany Department of Meteorology University of Reading Reading United Kingdom Geology Department University Centre in Svalbard Longyearbyen Norway Jet Propulsion Laboratory California Institute of Technology Wrightwood Calif

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A light-weight hot data identification scheme via grouping-based LRU lists 15th

A light-weight hot data identification scheme via grouping-b...

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： Shen, Biaobiao Li, Yongkun Xu, Yinlong Pan, Yubiao School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Guangdong Key Laboratory of Popular High Performance Computers Shenzhen518060 China Shenzhen Key Laboratory of Service Computing and Applications Shenzhen518060 China

ISBN: (纸本)9783319271392

Real-world workloads generally exhibit high skewness in access patterns, and it is a consensus that separating hot and cold data may greatly improve storage system performance such as Solid State Drive (SSD) garbage collection(GC) performance. To achieve this, the key issue is how to accurately identify hot data, which is really challenging due to the large diversity and dynamics of workloads. In this paper, we propose a light-weight and high-accuracy identification scheme, which is developed via a group of Least Recently Used (LRU) lists and requires only a small amount of memory and CPU cycles. We further deploy our scheme on SSDs with DiskSim simulator, and results show that comparing to two state-of-the-art identification schemes, our scheme further reduces SSD GC cost by up to 59.1% (62.1%), and saves 44.3% (77.5%) of computational cost. Due to the light-weight and parameter-insensitive feature, our scheme can be easily deployed at various system levels and adaptable to different workloads. © 2015 Springer International Publishing Switzerland.

关键词： Refuse collection

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